Optical Calculation Device and Optical Calculation Processing System

The present disclosure relates to an optical calculation system and an optical calculation processing system.

In recent years, there are increasing attentions to technologies to reduce communication capacity, by only transmitting metadata or feature amount similar thereto extracted by neural network.

Patent Literature 1: Japanese Unexamined Patent Application Publication No. 2019-191635

However, the extraction of feature amount by neural network has problems, such as high calculation cost and high power consumption on an edge device. Therefore, it is desirable to provide an optical calculation device, which allows suppression of calculation cost and energy consumption, and also to provide an optical calculation processing system provided therewith.

An optical calculation device according to a first aspect of a present disclosure is provided an optical neural network unit, a photodetection unit, and an output unit. The optical neural network unit is configured by hardware to output a feature amount map as an intensity distribution by encoding entering light. The photodetection unit generates image data by photodetecting the feature amount map. The output unit outputs the image data to an external communication network.

An optical calculation processing system according to a second aspect of the present disclosure is provided with an optical calculation device and an information processing device, communicatable with each other via a communication network. The optical calculation device is provided with an optical neural network unit, a photodetection unit, and an output unit. The optical neural network unit is configured by hardware to output a feature amount map as an intensity distribution by encoding entering light. The photodetection unit generates image data by photodetecting the feature amount map. The output unit outputs the image data to the information processing device via the communication network. The information processing device includes a processing unit that processes the image data acquired from the optical calculation device via the communication network.

According to the optical calculation device of to the first aspect of the present disclosure and the optical calculation processing system of the second aspect of the present disclosure, the optical neural network unit encodes entering light to generate the feature amount map, and the image data is generated by photodetecting the feature amount map in the photodetection unit. Thus, since the feature amount map is generated by the optical neural network unit before acquiring the image data in the photodetection unit, it is possible to perform information compression with low calculation cost and low power consumption.

1 FIG. 2 FIG. 1. Embodiment (and) 3 FIG. 16 FIG. 2. Modification Examples (to) 17 FIG. 18 FIG. 3. Application Example (and) Hereinafter, an embodiment for practicing the present disclosure is described in detail with reference to the drawings. It is to be noted that description is given in the following order.

1 FIG. 1 1 110 220 300 1 100 200 100 200 300 illustrates an overall configuration example of an optical calculation processing systemaccording to an embodiment of the present disclosure. The optical calculation processing systemis a low-communication-capacity type system, in which an optical neural network unitand a neural network unitare coupled with each other via a communication network. The optical calculation processing systemis provided with an optical calculation deviceand an information processing device. The optical calculation deviceand the information processing deviceare configured to allow communication with each other via the communication network.

100 110 120 130 200 210 220 230 130 210 300 300 The optical calculation deviceincludes the optical neural network unit, an image sensor, and an interface unit(an output unit). The information processing deviceincludes an interface unit, a neural network unit, and a calculation unit. The interface units,are configured by interfaces allowing communication with each other via the communication network. Any communication network may be used arbitrarily as the communication network, for example, a PAN (personal area network) such as USB, Blue Tooth (registered trademark), or a LAN (local area network) such as Ethernet (registered trademark), IEEE 802.11, or a WAN (wide area network).

110 120 110 111 111 The optical neural networkis provided at a front stage of the image sensor. The optical neural networkis configured by hardware, which outputs a feature amount map Lc as a light intensity distribution, corresponding to optical characteristics of a plurality of modulation elements, by encoding light inputted from an outside (for example, entering light La, or input image light Lb, which will be described afterwards) by a plurality of the modulation elements(optical modulation).

2 FIG. 111 111 Here, the “hardware” described above is configured, for example as illustrated in, by a plurality of the modulation elementsdisposed in a row via predetermined intervals. The modulation elementwill be explained in details afterwards.

2 FIG. 2 FIG. 2 FIG. 400 400 400 1 Among the entering light La entering from the outside, the input image light Lb is, for example as illustrated in, light transmitted through an aperture(transmitted light). The aperturehas an opening pattern, by which the transmitted light (input image light Lb) becomes light representing a numeral “0,” for example as illustrated in. Note that, the apertureis provided expediently in order to acquire the light representing the numeral “0” as the input image light Lb, for example as illustrated in, and is an arbitral component in the optical calculation processing system.

120 110 120 130 120 200 300 The image sensorgenerates image data Da by photodetecting the feature amount map Lc outputted from the optical neural network unit. The image sensoris a solid state imaging element, for example, CCD (charge coupled device), CMOS (complementary metal oxide semiconductor), etc. The interface unitoutputs the image data Da, which has been generated in the image sensor, to the information processing devicevia the communication network.

130 120 200 300 210 100 300 200 100 300 The interface unitoutputs the image data Da, which has been generated in the image sensor, to the information processing devicevia the communication network. The interface unitacquires the image data Da from the optical calculation devicevia the communication network. The information processing deviceprocesses the image data Da, which was acquired from the optical calculation devicevia the communication network.

220 110 120 130 300 210 220 220 100 The neural network unitis a calculation device coupled with the optical neural network unit, for example, via the image sensor, the interface unit, the communication network, and the interface unit. This calculation device is mounted with a neural network for realizing functions of the neural network unit. The neural network unitgenerates a reconstructed image data Db corresponding to the entering light La or the input image light Lb, for example by decoding the image data Da acquired from the optical calculation device.

230 220 230 230 230 230 230 The calculation unitis a calculation device for processing the reconstructed image data Db generated in the neural network unit. The calculation unitis configured, for example by including a CPU (central processing unit) and a GPU (graphics processing unit). The calculation unitis mounted with software for realizing functions of the calculation unit, and the calculation unitrealizes the functions of the calculation unitby executing the software.

111 111 111 Next, the modulation elementwill be explained. Each of the modulation elementsis, for example, a phase difference element or a metasurface. A plurality of the modulation elementsis formed by a physically mounted encoder acquired by causing learning model software to learn, which will be explained afterwards.

110 220 110 220 110 Here, the “learning model software” is a learning model, imitating a neural network, in which the optical neural network unitand the neural network unitare in an end-to-end coupling. In the “learning model software,” a neural network corresponding to the optical neural network unit, is mounted on the software as a physical model, which can learn actual optical systems based on physical laws, and specifically, is configured by optical diffraction calculations and phase modulation calculations, for which the learning has been performed by adjustment of phase modulation amounts. In the “learning model software,” a neural network corresponding to the neural network unit, realizes functions such as reconstruction and/or classification of images by utilizing a feature map as an input, which has been outputted from the optical calculation deviceas the image data Da, and the realization may be accomplished as a neural network such as a convolutional neural network or Transformer.

1 Next, effects of the optical calculation processing systemwill be explained.

In recent years, there are increasing attentions to technologies to reduce communication capacity, by only transmitting metadata extracted by neural network, or feature amount similar thereto. However, the extraction of feature amount by neural network has problems, such as a high calculation cost and a high power consumption on an edge device.

110 120 110 120 Meanwhile, according to the present embodiment, the optical neural network unitencodes the entering light La, whereby the feature amount map Lc is generated, and the image sensorphotodetects the feature amount map Lc, whereby the image data Da is generated. Thus, since the optical neural network unitgenerates the feature amount map Lc before the image sensoracquires the image data Da, it is possible to perform information compression with low calculation cost and low power consumption. Therefore, the calculation cost and the power consumption can be suppressed.

110 111 In the present embodiment, the optical neural network unitis configured by a plurality of phase difference elements. Thus, it is not necessary to store the image data Da as digital data in a memory, etc., and therefore, not only the suppression of calculation cost and power consumption, but furthermore, there is also an advantageous point from a viewpoint of security and/or privacy protection.

120 In the present embodiment, the image sensoris used for detecting the feature amount map Lc. Thus, it is possible to convert the feature amount map Lc efficiently, to the image data Da serving as the digital data.

220 200 In the present embodiment, the neural network unitis provided in the information processing device. Thus, it is possible to generate the reconstructed image data Db corresponding to the entering light La, by decoding the image data Da having a small data capacity. As a result, it is possible to realize data transmission at a low communication capacity.

1 Next, modification examples of the optical calculation processing systemaccording to the embodiment described above, will be explained. In the following examples, common reference signs will be assigned to common components, and explanations of the common components will be omitted in a proper manner.

200 220 220 220 220 220 220 220 3 FIG. In the above embodiment the information processing devicemay include, for example as illustrated in, a plurality of neural networks(for example,A,B,C). Note that, in the present modification example, it is also possible to provide, for example, at least two of the neural networksA,B,C.

220 220 220 210 220 220 220 220 220 220 230 The neural networksA,B,C are coupled, in parallel with each other, with an output terminal of the interface unit. The image data Da is inputted to the neural networksA,B,C. Outputs from the neural networksA,B,C are inputted, for example into the common calculation unit.

220 220 220 110 120 130 300 210 220 220 220 220 220 220 The neural networksA,B,C are calculation devices coupled with the optical neural network unit, for example, via the image sensor, the interface unit, the communication network, and the interface unit. The calculation device serving as the neural networkA is mounted with a neural network for realizing functions of the neural network unitA. The calculation device serving as the neural networkB is mounted with a neural network for realizing functions of the neural network unitB. The calculation device serving as the neural networkC is mounted with a neural network for realizing functions of the neural network unitC.

220 100 220 100 220 100 The neural network unitA generates a reconstructed image data DbA corresponding to the entering light La or the input image light Lb, for example by decoding the image data Da acquired from the optical calculation device. The neural network unitB generates a character classification DbB (for example, a numeral “0”) included in the entering light La or the input image light Lb, for example by decoding the image data Da acquired from the optical calculation device. The neural network unitC generates a character handwriting DbC (for example, a handwriting of Mr. Adam) included in the entering light La or the input image light Lb, for example by decoding the image data Da acquired from the optical calculation device.

230 220 220 220 220 The calculation unitprocesses data (for example, the reconstructed image data DbA, the classification DbB, the handwriting DbC) inputted from a plurality of the neural networks(for example,A,B,C).

220 100 230 In the present modification example, a plurality of the neural networksdecodes the image data Da acquired from the optical calculation device, whereby a plurality of data (for example, the reconstructed image data DbA, the classification DbB, the handwriting DbC) is generated. Thus, it is possible to perform processing by using at least one of the plurality of data in the calculation unit.

100 140 110 4 FIG. 4 FIG. In the above embodiment and the modification example, the optical calculation devicemay include, for example as illustrated in, a light projection unit, which generates irradiation light Ld, and further generates the entering light La by reflected light of the irradiation light Ld. For example, as illustrated in, an object OB is irradiated with the irradiation light Ld, whereby the reflected light is generated from the object OB. This reflected light serves as the entering light La, and enters the optical neural network.

The irradiation light Ld may be in a form of, for example, light at a single wavelength, or light including a plurality of wavelengths (for example, light including red light, green light, and blue light; or light including wavelengths covering a whole range of visible region). The irradiation light Ld may be in a form of, for example, monochromatic light, or white light. The irradiation light Ld may be in a form of, for example, spherical wave light, or collimated light. The irradiation light Ld may be in a form of, for example, unpolarized light, or linearly polarized light.

140 140 The light projection unitmay be in a form of, for example, a point light source, or a light source from which structured light can be outputted. The light projection unitmay, for example, output the irradiation light Ld continuously in time, or output the irradiation light Ld intermittently in time (i.e. in a form of pulse).

110 230 230 230 230 In the present modification example, the reflected light, generated by irradiation of the irradiation light Ld onto the object OB, serves as the entering light La and enters the optical neural network. Thus, it is possible to acquire the image data Da and the reconstructed image data Db corresponding to characteristics of the irradiation light Ld. Consequently, the calculation unitmay reconstruct a three-dimensional shape of the object OB based on the reconstructed image data Db. Moreover, the calculation unitmay also estimate attitude or orientation of the object OB based on the reconstructed image data Db. Moreover, the calculation unitmay also estimate material of the object OB based on the reconstructed image data Db. In a case that the object OB is composed by a plurality of members, respectively made of different materials, the calculation unitmay estimate the materials of the object OB at each position of the object OB, based on the reconstructed image data Db.

100 5 FIG. In the above embodiment and the modification example, the optical calculation devicemay be configured, for example as illustrated in, to generate a plurality of pieces of reflected lights by applying the irradiation light Ld from a plurality of directions onto the object OB.

100 140 110 120 110 150 150 120 200 300 5 FIG. In this example, the optical calculation deviceincludes, for example as illustrated in, a plurality of light projection units, a plurality of optical neural network unitsprovided one by one for each of the pieces of reflected lights, a plurality of image sensorsprovided one by one for each of the optical neural network units, and an interface unit. The interface unitoutputs a plurality of image data Da, acquired one by one from each of the image sensor, to the information processing devicevia the communication network.

200 210 300 220 220 230 220 In the information processing device, the interface unitbinds a plurality of the image data Da inputted via the communication network, and inputs acquired image data Dc to the neural network. The neural networkdecodes the image data Dc, and accordingly, estimates the three-dimensional shape of the object OB. The calculation unitprocesses the three-dimensional data of the object OB acquired in the neural network.

110 120 200 300 200 In the present modification example, a plurality of the image data Da, acquired one by one from each module including the optical neural network unitand the image sensor, is outputted to the information processing devicevia the communication network. Thus, based on the image data Dc, it is possible to perform more complicated processing in the information processing device.

100 160 161 160 6 FIG. In the above embodiment and the modification example, the optical calculation devicemay include, for example as illustrated in, an optical-address-type spatial light modulation element, and a light source unit, which applies coherent light Le onto the optical-address-type spatial light modulation element.

160 The optical-address-type spatial light modulation elementis an optical element, composed by material of which optical characteristics change by irradiation of the coherent light Le. As examples of “material of which optical characteristics change” described above, photorefractive material, or photochromic material, may be used.

160 110 6 FIG. The optical-address-type spatial light modulation elementhas a configuration, for example as illustrated in, in which the irradiation of the coherent light Le generates synthetic light (coherent light including phase information), by synthesizing the coherent light Le with incoherent light Lf, which is external light (sunlight or indoor light). This synthetic light serves as the entering light La and enters the optical neural network.

160 In the present modification example, the entering light La is generated by using the optical-address-type spatial light modulation element. Thus, it is possible to give nonlinear characteristics to a relation between an optical electric field and a phase, and accordingly, it is possible to generate the reconstructed image data Db accurately, corresponding to the entering light La or the input image light Lb.

111 110 110 7 FIG. 7 FIG. In the above embodiment and the modification example, a plurality of the modulation elementsmay be formed by birefringent material. In this case, for example as illustrated in, where longitudinally polarized light serving as the entering light La enters the optical neural network, the feature amount map Lc and the image data Da corresponding to the longitudinal polarization are generated, and the reconstructed image data Db corresponding to the longitudinal polarization is generated. Moreover, for example as illustrated in, where laterally polarized light serving as the entering light La enters the optical neural network, the feature amount map Lc and the image data Da corresponding to the lateral polarization are generated, and the reconstructed image data Db corresponding to the lateral polarization is generated. Therefore, in the present modification example, it is possible to extract information related to polarization, and accordingly it is possible, for example, to perform reconstruction of images under the longitudinal polarization and the lateral polarization, respectively, and/or estimation of surface condition based on a ratio of polarization directions.

100 170 120 120 8 FIG. In the above embodiment and the modification example, the optical calculation devicemay include, for example as illustrated in, a photodetector arrayinstead of the image sensor. In this example, as compared with the image sensor, it is possible to increase the reading speed, whereby it is possible to increase the overall driving speed.

100 180 110 180 120 170 180 181 181 9 FIG. 10 FIG. 11 FIG. In the above embodiment and the modification example, the optical calculation devicemay include, for example as illustrated inand, an optical neural networkinstead of the optical neural network. The optical neural networkis provided at a front stage of the image sensoror the photodetector array. The optical neural network unitis configured, for example as illustrated in, by hardware, which outputs the feature amount map Lc as a light intensity distribution, corresponding to optical characteristics of a plurality of modulation elements, by encoding light inputted from an outside (for example, the entering light La, or the input image light Lb) by a plurality of the modulation elements(optical modulation).

181 181 Each of the modulation elementsis, for example, a spatial light modulation liquid crystal element or a MEMS mirror. A plurality of the modulation elementsis formed by a physically mounted encoder acquired by causing learning model software to learn, which will be explained afterwards.

180 220 180 220 Here, the “learning model software” is a learning model, imitating a neural network, in which the optical neural network unitand the neural network unitare in an end-to-end coupling. In the “learning model software,” a neural network corresponding to the optical neural network unitincludes, for example, an input layer, an intermediate layer, and an output layer. In the “learning model software,” a neural network corresponding to the neural network unitincludes, for example, an input layer, an intermediate layer, and an output layer.

Each layer is provided with one neuron or a plurality of neurons. The neurons in the adjacent layers are coupled to each other, and a weight (coupling load) is set to each coupling. The number of couplings of neurons may be set suitably. A threshold has been set for each neuron, and for example, an output value of each neuron is determined in accordance with a result of whether or not the sum of the product of each input value to each neuron and the weight exceeds the threshold.

100 190 181 190 181 200 180 180 11 FIG. In the present modification example, the optical calculation devicefurther includes, for example as illustrated in, a drive unitfor switching the weight of a plurality of the modulation elements. The drive unitswitches the weight of a plurality of the modulation elementsbased on a control signal Dout inputted from the information processing device. Thus, every time when the weight of a plurality of the modulation elementsis switched, the optical neural networkoutputs a fresh feature amount map Lc as the light intensity distribution.

200 240 230 240 100 500 300 500 500 300 12 FIG. In the present modification example, the information processing deviceincludes, for example as illustrated in, an interface unitfor generating the control signal Dout based on the reconstructed image data Db acquired from the calculation unit. The interface unitoutputs the generated control signal Dout to the optical calculation devicevia a communication network. Note that, the communication networkmay also serve as the communication network, otherwise the communication networkmay be provided separately from the communication network.

181 180 220 In the present modification example, a plurality of the modulation elementsis provided, which allows switching of the weight. Thus, it is possible to accomplish high graduation of the reconstructed image data Db. Moreover, it is also possible to cause the optical neural network unitto learn successively based on the output of the neural network unit(decoder), and therefore, it is also possible to generate a highly accurate reconstructed image data Db.

13 FIG. 14 FIG. 13 FIG. 14 FIG. 1 100 1 600 700 andillustrate a modification example of the optical calculation processing systemprovided with the optical calculation deviceaccording to the modification example G described above. In the above modification example G, the optical calculation processing systemmay include, for example as illustrated inand, a calculation device, and an image display device.

700 180 700 600 700 100 180 700 The image display devicegenerates various types of image light (input image light Lb), required for machine learning given to the optical neural network. The image display devicegenerates, for example various types of image light (input image light Lb), based on a control signal Ctrl inputted from the calculation device. The image display deviceoutputs, for example various types of generated image light (input image light Lb), to the optical calculation device(optical neural network unit). The image display deviceis configured, for example by including a liquid crystal display panel or an organic EL display panel.

600 181 180 600 220 220 220 220 The calculation deviceadjusts, by machine learning, a phase amount (weight) of each of the modulation elementsincluded in the optical neural network. With the adjustment of the phase amount by the calculation device, for example, it is possible to perform highly accurate image reconstruction in the neural network unit(orA), it is possible to perform highly accurate classification of characters in the neural network unitB, and/or it is possible to perform highly accurate determination of handwritings in the neural network unitC.

600 610 620 630 640 650 600 600 600 600 15 FIG. The calculation deviceincludes, for example as illustrated in, an image acquisition unit, a model calculation unit, a gradient calculation unit, a phase update unit, and a drive unit. The calculation deviceis mounted with software for realizing functions of the calculation device, and the calculation devicerealizes the functions of the calculation deviceby executing the software.

600 600 600 600 The calculation devicecan be implemented by a circuit including at least one semiconductor integrated circuit, such as at least one processor (for example, central processing unit (CPU)), at least one application-specific integrated circuit (ASIC), and/or at least one field programmable gate array (FPGA). At least one processor can be configured to execute all or part of the functions of the calculation device, by reading instructions from at least one non-transitory and tangible computer readable medium. As for these media, various forms may be used, for example but not limited to, various magnetic media such as hard disks, various optical media such as CDs and DVDs, or various semiconductor memories (i.e. semiconductor circuits) such as volatile memories or non-volatile memories. The volatile memories may include DRAMs and SRAMs. The non-volatile memories may include ROMs and NVRAMs. ASIC is an integrated circuit (IC) specialized for executing all or part of the functions of the calculation device. FPGA is an integrated circuit, allowing after-manufactured configuration to execute all or part of the functions of the calculation device.

610 180 220 630 1 The image acquisition unitacquires the image data Da, Db from the neural network units,, and outputs the acquired image data Da, Db to the gradient calculation unit, as light intensity distribution data I.

620 630 181 620 2 2 15 FIG. The model calculation unitgenerates light intensity distribution data I(image data) for learning purpose, for example based on formula (1), formula (2), formula (3), and formula (4) shown in, and outputs the data to the gradient calculation unit. Each of the formula (1), formula (2), formula (3), and formula (4) is Rayleigh-Sommerfeld Formula. In the formula (1), formula (2), formula (3), and formula (4), x and y represent coordinates of each of pixels of the modulation element. A generation method of light intensity distribution data Iin the model calculation unitis not limited to the above explanation. L represents loss function, and o represents phase distribution.

h (x, y, z): Integration Kernel of Rayleigh-Sommerfeld Formula; φ: Phase distribution displayed in SLM; λ: Wavelength of light. Here, the formula (1) is a formula for calculating intensity distribution from optical complex amplitude distribution. The formula (2) is Rayleigh-Sommerfeld Formula. The formula (3) is an auxiliary formula of the formula (2). The formula (4) is an auxiliary formula of the formula (3). Moreover, the meaning of each symbol is as below:

630 610 620 630 640 1 2 15 FIG. The gradient calculation unitderives a gradient (differential value (δL/δφ)) relative to the phase distribution of the loss function L, by inputting, for example the light intensity distribution data Iacquired from the image acquisition unit, and the light intensity distribution data Iacquired from the model calculation unit, into formula (5) of. The gradient calculation unitoutputs, for example the derived differential value (δL/δφ) to the phase update unit.

2 1 620 610 On the right side of the formula (5), in the item on the right side, the light intensity distribution data Iin the model calculation unitis used as the light intensity distribution data. This is because, the question of how the minute change of phase distribution affects the actually-measured intensity distribution, is in a black box. On the right side of the formula (5), in the item on the left side, the light intensity distribution data Iacquired from the image acquisition unitis used as the light intensity distribution data. This is because, on the right side of the formula (5), the item on the left side shows how the minute change of intensity distribution affects the loss function L, and therefore, strict calculation can be performed by using the actually-measured intensity distribution.

640 630 640 640 650 15 FIG. The phase update unitupdates the phase amount acquired at a preceding step, based on the differential value (δL/δφ) acquired from the gradient calculation unit. The phase update unitupdates the phase amount acquired at the precedent step, for example based on formula (6) of. In the formula (6), γ represents learning ratio. The phase update unitoutputs, for example the derived phase amount, to the drive unit.

650 181 180 180 2 650 181 180 640 The drive unitoutputs the phase amount of each of the modulation elementsincluded in the optical neural network, to the optical neural networkas a control signal Ctr. Thus, the drive unitsets the phase amount of each of the modulation elementsincluded in the optical neural network, as the phase amount acquired from the phase update unit.

600 181 180 220 220 220 220 In the present modification example, the calculation deviceadjusts, by machine learning, the phase amount of each of the modulation elementsincluded in the optical neural network. Thus, for example, it is possible to perform highly accurate image reconstruction in the neural network unit(orA), it is possible to perform highly accurate classification of characters in the neural network unitB, and/or it is possible to perform highly accurate determination of handwritings in the neural network unitC.

16 FIG. 16 FIG. 600 600 620 660 630 illustrates a modification example of the calculation deviceaccording to the modification example H described above. With reference to the above modification example H, for example as illustrated in, the calculation deviceis equivalent to a device, in which, the model calculation unitis omitted, and a gradient calculation unitis provided instead of the gradient calculation unit.

181 180 181 181 181 181 181 181 660 1 In the present modification example, each of the modulation elementsincluded in the optical neural networkis an element, which can drive each pixel at a high speed. For example, it is assumed that each of the modulation elementsis composed on pixels of 500×500. In this case, where the phase amount of each pixel of each of the modulation elementsis changed one by one, 500×500=250,000 images can be obtained. For example, if each of the modulation elementscan drive each pixel at a drive frequency of 1 GHz, each of the modulation elementscan output 250,000 images at 0.25 ms. Thus, where each of the modulation elementsis the element, which can drive each pixel at a high speed, then, by minutely changing the phase amount one by one, of each pixel of each of the modulation elements, the gradient calculation unitcan directly calculate δI/δφ (see formula (8)).

660 610 660 640 1 16 FIG. The gradient calculation unitderives a gradient (differential value (δL/δφ)) relative to the phase distribution of the loss function L, by inputting, for example the light intensity distribution data Iacquired from the image acquisition unit, into formula (7) and formula (8) of. The gradient calculation unitoutputs, for example the derived differential value (δL/δφ) to the phase update unit.

1 In the present modification example, δI/δφ can be obtained based on the actually-measured image. Thus, as compared with the modification example H described above, it is possible to improve performance of image reconstruction, character classification, and/or handwriting determination.

1 Next, an application example of the optical calculation processing systemaccording to the above embodiment will be explained.

The technology according to the present disclosure can be applied to various products. For example, the technology according to the present disclosure may be realized as a device installed in any kind of mobile bodies, such as automobiles, electric vehicles, hybrid electric vehicles, motorcycles, bicycles, personal mobilities, aircrafts, drones, ships, robots, construction machines, agricultural machines, and/or tractors.

17 FIG. 17 FIG. 7000 7000 7010 7000 7100 7200 7300 7400 7500 7600 7010 is a block diagram depicting an example of schematic configuration of a vehicle control systemas an example of a mobile body control system to which the technology according to an embodiment of the present disclosure can be applied. The vehicle control systemincludes a plurality of electronic control units connected to each other via a communication network. In the example depicted in, the vehicle control systemincludes a driving system control unit, a body system control unit, a battery control unit, an outside-vehicle information detecting unit, an in-vehicle information detecting unit, and an integrated control unit. The communication networkconnecting the plurality of control units to each other may, for example, be a vehicle-mounted communication network compliant with an arbitrary standard such as controller area network (CAN), local interconnect network (LIN), local area network (LAN), FlexRay (registered trademark), or the like.

7010 7600 7610 7620 7630 7640 7650 7660 7670 7680 7690 17 FIG. Each of the control units includes: a microcomputer that performs arithmetic processing according to various kinds of programs; a storage section that stores the programs executed by the microcomputer, parameters used for various kinds of operations, or the like; and a driving circuit that drives various kinds of control target devices. Each of the control units further includes: a network interface (I/F) for performing communication with other control units via the communication network; and a communication I/F for performing communication with a device, a sensor, or the like within and without the vehicle by wire communication or radio communication. A functional configuration of the integrated control unitillustrated inincludes a microcomputer, a general-purpose communication I/F, a dedicated communication I/F, a positioning section, a beacon receiving section, an in-vehicle device I/F, a sound/image output section, a vehicle-mounted network I/F, and a storage section. The other control units similarly include a microcomputer, a communication I/F, a storage section, and the like.

7100 7100 7100 The driving system control unitcontrols the operation of devices related to the driving system of the vehicle in accordance with various kinds of programs. For example, the driving system control unitfunctions as a control device for a driving force generating device for generating the driving force of the vehicle, such as an internal combustion engine, a driving motor, or the like, a driving force transmitting mechanism for transmitting the driving force to wheels, a steering mechanism for adjusting the steering angle of the vehicle, a braking device for generating the braking force of the vehicle, and the like. The driving system control unitmay have a function as a control device of an antilock brake system (ABS), electronic stability control (ESC), or the like.

7100 7110 7110 7100 7110 The driving system control unitis connected with a vehicle state detecting section. The vehicle state detecting section, for example, includes at least one of a gyro sensor that detects the angular velocity of axial rotational movement of a vehicle body, an acceleration sensor that detects the acceleration of the vehicle, and sensors for detecting an amount of operation of an accelerator pedal, an amount of operation of a brake pedal, the steering angle of a steering wheel, an engine speed or the rotational speed of wheels, and the like. The driving system control unitperforms arithmetic processing using a signal input from the vehicle state detecting section, and controls the internal combustion engine, the driving motor, an electric power steering device, the brake device, and the like.

7200 7200 7200 7200 The body system control unitcontrols the operation of various kinds of devices provided to the vehicle body in accordance with various kinds of programs. For example, the body system control unitfunctions as a control device for a keyless entry system, a smart key system, a power window device, or various kinds of lamps such as a headlamp, a backup lamp, a brake lamp, a turn signal, a fog lamp, or the like. In this case, radio waves transmitted from a mobile device as an alternative to a key or signals of various kinds of switches can be input to the body system control unit. The body system control unitreceives these input radio waves or signals, and controls a door lock device, the power window device, the lamps, or the like of the vehicle.

7300 7310 7300 7310 7300 7310 The battery control unitcontrols a secondary battery, which is a power supply source for the driving motor, in accordance with various kinds of programs. For example, the battery control unitis supplied with information about a battery temperature, a battery output voltage, an amount of charge remaining in the battery, or the like from a battery device including the secondary battery. The battery control unitperforms arithmetic processing using these signals, and performs control for regulating the temperature of the secondary batteryor controls a cooling device provided to the battery device or the like.

7400 7000 7400 7410 7420 7410 7420 7000 The outside-vehicle information detecting unitdetects information about the outside of the vehicle including the vehicle control system. For example, the outside-vehicle information detecting unitis connected with at least one of an imaging sectionand an outside-vehicle information detecting section. The imaging sectionincludes at least one of a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, and other cameras. The outside-vehicle information detecting section, for example, includes at least one of an environmental sensor for detecting current atmospheric conditions or weather conditions and a peripheral information detecting sensor for detecting another vehicle, an obstacle, a pedestrian, or the like on the periphery of the vehicle including the vehicle control system.

7410 7420 The environmental sensor, for example, may be at least one of a rain drop sensor detecting rain, a fog sensor detecting a fog, a sunshine sensor detecting a degree of sunshine, and a snow sensor detecting a snowfall. The peripheral information detecting sensor may be at least one of an ultrasonic sensor, a radar device, and a LIDAR device (Light detection and Ranging device, or Laser imaging detection and ranging device). Each of the imaging sectionand the outside-vehicle information detecting sectionmay be provided as an independent sensor or device, or may be provided as a device in which a plurality of sensors or devices are integrated.

18 FIG. 7410 7420 7910 7912 7914 7916 7918 7900 7910 7918 7900 7912 7914 7900 7916 7900 7918 depicts an example of installation positions of the imaging sectionand the outside-vehicle information detecting section. Imaging sections,,,, andare, for example, disposed at at least one of positions on a front nose, sideview mirrors, a rear bumper, and a back door of the vehicleand a position on an upper portion of a windshield within the interior of the vehicle. The imaging sectionprovided to the front nose and the imaging sectionprovided to the upper portion of the windshield within the interior of the vehicle obtain mainly an image of the front of the vehicle. The imaging sectionsandprovided to the sideview mirrors obtain mainly an image of the sides of the vehicle. The imaging sectionprovided to the rear bumper or the back door obtains mainly an image of the rear of the vehicle. The imaging sectionprovided to the upper portion of the windshield within the interior of the vehicle is used mainly to detect a preceding vehicle, a pedestrian, an obstacle, a signal, a traffic sign, a lane, or the like.

18 FIG. 7910 7912 7914 7916 7910 7912 7914 7916 7900 7910 7912 7914 7916 Incidentally,depicts an example of photographing ranges of the respective imaging sections,,, and. An imaging range a represents the imaging range of the imaging sectionprovided to the front nose. Imaging ranges b and c respectively represent the imaging ranges of the imaging sectionsandprovided to the sideview mirrors. An imaging range d represents the imaging range of the imaging sectionprovided to the rear bumper or the back door. A bird's-eye image of the vehicleas viewed from above can be obtained by superimposing image data imaged by the imaging sections,,, and, for example.

7920 7922 7924 7926 7928 7930 7900 7920 7926 7930 7900 7900 7920 7930 Outside-vehicle information detecting sections,,,,, andprovided to the front, rear, sides, and corners of the vehicleand the upper portion of the windshield within the interior of the vehicle may be, for example, an ultrasonic sensor or a radar device. The outside-vehicle information detecting sections,, andprovided to the front nose of the vehicle, the rear bumper, the back door of the vehicle, and the upper portion of the windshield within the interior of the vehicle may be a LIDAR device, for example. These outside-vehicle information detecting sectionstoare used mainly to detect a preceding vehicle, a pedestrian, an obstacle, or the like.

17 FIG. 7400 7410 7400 7420 7400 7420 7400 7400 7400 7400 Returning to, the description will be continued. The outside-vehicle information detecting unitmakes the imaging sectionimage an image of the outside of the vehicle, and receives imaged image data. In addition, the outside-vehicle information detecting unitreceives detection information from the outside-vehicle information detecting sectionconnected to the outside-vehicle information detecting unit. In a case where the outside-vehicle information detecting sectionis an ultrasonic sensor, a radar device, or a LIDAR device, the outside-vehicle information detecting unittransmits an ultrasonic wave, an electromagnetic wave, or the like, and receives information of a received reflected wave. On the basis of the received information, the outside-vehicle information detecting unitmay perform processing of detecting an object such as a human, a vehicle, an obstacle, a sign, a character on a road surface, or the like, or processing of detecting a distance thereto. The outside-vehicle information detecting unitmay perform environment recognition processing of recognizing a rainfall, a fog, road surface conditions, or the like on the basis of the received information. The outside-vehicle information detecting unitmay calculate a distance to an object outside the vehicle on the basis of the received information.

7400 7400 7410 7400 7410 In addition, on the basis of the received image data, the outside-vehicle information detecting unitmay perform image recognition processing of recognizing a human, a vehicle, an obstacle, a sign, a character on a road surface, or the like, or processing of detecting a distance thereto. The outside-vehicle information detecting unitmay subject the received image data to processing such as distortion correction, alignment, or the like, and combine the image data imaged by a plurality of different imaging sectionsto generate a bird's-eye image or a panoramic image. The outside-vehicle information detecting unitmay perform viewpoint conversion processing using the image data imaged by the imaging sectionincluding the different imaging parts.

7500 7500 7510 7510 7510 7500 7500 The in-vehicle information detecting unitdetects information about the inside of the vehicle. The in-vehicle information detecting unitis, for example, connected with a driver state detecting sectionthat detects the state of a driver. The driver state detecting sectionmay include a camera that images the driver, a biosensor that detects biological information of the driver, a microphone that collects sound within the interior of the vehicle, or the like. The biosensor is, for example, disposed in a seat surface, the steering wheel, or the like, and detects biological information of an occupant sitting in a seat or the driver holding the steering wheel. On the basis of detection information input from the driver state detecting section, the in-vehicle information detecting unitmay calculate a degree of fatigue of the driver or a degree of concentration of the driver, or may determine whether the driver is dozing. The in-vehicle information detecting unitmay subject an audio signal obtained by the collection of the sound to processing such as noise canceling processing or the like.

7600 7000 7600 7800 7800 7600 7800 7000 7800 7800 7800 7600 7000 7800 The integrated control unitcontrols general operation within the vehicle control systemin accordance with various kinds of programs. The integrated control unitis connected with an input section. The input sectionis implemented by a device capable of input operation by an occupant, such, for example, as a touch panel, a button, a microphone, a switch, a lever, or the like. The integrated control unitmay be supplied with data obtained by voice recognition of voice input through the microphone. The input sectionmay, for example, be a remote control device using infrared rays or other radio waves, or an external connecting device such as a mobile telephone, a personal digital assistant (PDA), or the like that supports operation of the vehicle control system. The input sectionmay be, for example, a camera. In that case, an occupant can input information by gesture. Alternatively, data may be input which is obtained by detecting the movement of a wearable device that an occupant wears. Further, the input sectionmay, for example, include an input control circuit or the like that generates an input signal on the basis of information input by an occupant or the like using the above-described input section, and which outputs the generated input signal to the integrated control unit. An occupant or the like inputs various kinds of data or gives an instruction for processing operation to the vehicle control systemby operating the input section.

7690 7690 The storage sectionmay include a read only memory (ROM) that stores various kinds of programs executed by the microcomputer and a random access memory (RAM) that stores various kinds of parameters, operation results, sensor values, or the like. In addition, the storage sectionmay be implemented by a magnetic storage device such as a hard disc drive (HDD) or the like, a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like.

7620 7750 7620 7620 7620 2 The general-purpose communication I/Fis a communication I/F used widely, which communication I/F mediates communication with various apparatuses present in an external environment. The general-purpose communication I/Fmay implement a cellular communication protocol such as global system for mobile communications (GSM (registered trademark)), worldwide interoperability for microwave access (WiMAX (registered trademark)), long term evolution (LTE (registered trademark)), LTE-advanced (LTE-A), or the like, or another wireless communication protocol such as wireless LAN (referred to also as wireless fidelity (Wi-Fi (registered trademark)), Bluetooth (registered trademark), or the like. The general-purpose communication I/Fmay, for example, connect to an apparatus (for example, an application server or a control server) present on an external network (for example, the Internet, a cloud network, or a company-specific network) via a base station or an access point. In addition, the general-purpose communication I/Fmay connect to a terminal present in the vicinity of the vehicle (which terminal is, for example, a terminal of the driver, a pedestrian, or a store, or a machine type communication (MTC) terminal) using a peer to peer (PP) technology, for example.

7630 7630 7630 The dedicated communication I/Fis a communication I/F that supports a communication protocol developed for use in vehicles. The dedicated communication I/Fmay implement a standard protocol such, for example, as wireless access in vehicle environment (WAVE), which is a combination of institute of electrical and electronic engineers (IEEE) 802.11p as a lower layer and IEEE 1609 as a higher layer, dedicated short range communications (DSRC), or a cellular communication protocol. The dedicated communication I/Ftypically carries out V2X communication as a concept including one or more of communication between a vehicle and a vehicle (Vehicle to Vehicle), communication between a road and a vehicle (Vehicle to Infrastructure), communication between a vehicle and a home (Vehicle to Home), and communication between a pedestrian and a vehicle (Vehicle to Pedestrian).

7640 7640 The positioning section, for example, performs positioning by receiving a global navigation satellite system (GNSS) signal from a GNSS satellite (for example, a GPS signal from a global positioning system (GPS) satellite), and generates positional information including the latitude, longitude, and altitude of the vehicle. Incidentally, the positioning sectionmay identify a current position by exchanging signals with a wireless access point, or may obtain the positional information from a terminal such as a mobile telephone, a personal handyphone system (PHS), or a smart phone that has a positioning function.

7650 7650 7630 The beacon receiving section, for example, receives a radio wave or an electromagnetic wave transmitted from a radio station installed on a road or the like, and thereby obtains information about the current position, congestion, a closed road, a necessary time, or the like. Incidentally, the function of the beacon receiving sectionmay be included in the dedicated communication I/Fdescribed above.

7660 7610 7760 7660 7660 7760 7760 7660 7760 The in-vehicle device I/Fis a communication interface that mediates connection between the microcomputerand various in-vehicle devicespresent within the vehicle. The in-vehicle device I/Fmay establish wireless connection using a wireless communication protocol such as wireless LAN, Bluetooth (registered trademark), near field communication (NFC), or wireless universal serial bus (WUSB). In addition, the in-vehicle device I/Fmay establish wired connection by universal serial bus (USB), high-definition multimedia interface (HDMI (registered trademark)), mobile high-definition link (MHL), or the like via a connection terminal (and a cable if necessary) not depicted in the figures. The in-vehicle devicesmay, for example, include at least one of a mobile device and a wearable device possessed by an occupant and an information device carried into or attached to the vehicle. The in-vehicle devicesmay also include a navigation device that searches for a path to an arbitrary destination. The in-vehicle device I/Fexchanges control signals or data signals with these in-vehicle devices.

7680 7610 7010 7680 7010 The vehicle-mounted network I/Fis an interface that mediates communication between the microcomputerand the communication network. The vehicle-mounted network I/Ftransmits and receives signals or the like in conformity with a predetermined protocol supported by the communication network.

7610 7600 7000 7620 7630 7640 7650 7660 7680 7610 7100 7610 7610 The microcomputerof the integrated control unitcontrols the vehicle control systemin accordance with various kinds of programs on the basis of information obtained via at least one of the general-purpose communication I/F, the dedicated communication I/F, the positioning section, the beacon receiving section, the in-vehicle device I/F, and the vehicle-mounted network I/F. For example, the microcomputermay calculate a control target value for the driving force generating device, the steering mechanism, or the braking device on the basis of the obtained information about the inside and outside of the vehicle, and output a control command to the driving system control unit. For example, the microcomputermay perform cooperative control intended to implement functions of an advanced driver assistance system (ADAS) which functions include collision avoidance or shock mitigation for the vehicle, following driving based on a following distance, vehicle speed maintaining driving, a warning of collision of the vehicle, a warning of deviation of the vehicle from a lane, or the like. In addition, the microcomputermay perform cooperative control intended for automated driving, which makes the vehicle to travel automatedly without depending on the operation of the driver, or the like, by controlling the driving force generating device, the steering mechanism, the braking device, or the like on the basis of the obtained information about the surroundings of the vehicle.

7610 7620 7630 7640 7650 7660 7680 7610 The microcomputermay generate three-dimensional distance information between the vehicle and an object such as a surrounding structure, a person, or the like, and generate local map information including information about the surroundings of the current position of the vehicle, on the basis of information obtained via at least one of the general-purpose communication I/F, the dedicated communication I/F, the positioning section, the beacon receiving section, the in-vehicle device I/F, and the vehicle-mounted network I/F. In addition, the microcomputermay predict danger such as collision of the vehicle, approaching of a pedestrian or the like, an entry to a closed road, or the like on the basis of the obtained information, and generate a warning signal. The warning signal may, for example, be a signal for producing a warning sound or lighting a warning lamp.

7670 7710 7720 7730 7720 7720 7610 17 FIG. The sound/image output sectiontransmits an output signal of at least one of a sound and an image to an output device capable of visually or auditorily notifying information to an occupant of the vehicle or the outside of the vehicle. In the example of, an audio speaker, a display section, and an instrument panelare illustrated as the output device. The display sectionmay, for example, include at least one of an on-board display and a head-up display. The display sectionmay have an augmented reality (AR) display function. The output device may be other than these devices, and may be another device such as headphones, a wearable device such as an eyeglass type display worn by an occupant or the like, a projector, a lamp, or the like. In a case where the output device is a display device, the display device visually displays results obtained by various kinds of processing performed by the microcomputeror information received from another control unit in various forms such as text, an image, a table, a graph, or the like. In addition, in a case where the output device is an audio output device, the audio output device converts an audio signal constituted of reproduced audio data or sound data or the like into an analog signal, and auditorily outputs the analog signal.

7010 7000 7010 7010 17 FIG. Incidentally, at least two control units connected to each other via the communication networkin the example depicted inmay be integrated into one control unit. Alternatively, each individual control unit may include a plurality of control units. Further, the vehicle control systemmay include another control unit not depicted in the figures. In addition, part or the whole of the functions performed by one of the control units in the above description may be assigned to another control unit. That is, predetermined arithmetic processing may be performed by any of the control units as long as information is transmitted and received via the communication network. Similarly, a sensor or a device connected to one of the control units may be connected to another control unit, and a plurality of control units may mutually transmit and receive detection information via the communication network.

1 1 16 FIGS.to Note that a computer program for achieving each function of the optical calculation processing systemdescribed with reference toand the like can be implemented in any one of the control units and the like. In addition, a computer-readable recording medium in which such a computer program is stored may be provided.

The recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, or the like. In addition, the computer program described above may be distributed, for example, through a network without using a recording medium.

7000 1 1 1 1 FIG. 16 FIG. 1 FIG. 16 FIG. 1 FIG. 16 FIG. In the vehicle control systemexplained above, the optical calculation processing systemexplained with reference totomay be used, for example, as a light source steering unit of LIDAR as an environment sensor. Moreover, the image recognition in the imaging unit can be performed in an optical computing unit using the optical calculation processing system, which has been explained with reference toto, etc. In a case that the optical calculation processing system, which has been explained with reference toto, etc., is used as a high-efficiency, high-luminance projection device, it is possible to project lines and characters on a ground. In particular, it is possible to display lines so that people outside a vehicle can understand a passing route of the vehicle when the vehicle is in a reverse motion, and/or it is possible to display a pedestrian crossing by light, when the vehicle gives way to the pedestrian.

1 7600 1 7000 1 FIG. 16 FIG. 17 FIG. 1 FIG. 16 FIG. 17 FIG. Moreover, at least one component of the optical calculation processing system, which has been explained with reference toto, etc., may be realized in a module (for example, an integrated circuit module configured by one die) for the integrated control unitas illustrated in. Furthermore, the optical calculation processing system, which has been explained with reference toto, etc., may be realized by a plurality of the control units of the vehicle control systemas illustrated in.

Although the present disclosure has been described with reference to the embodiments, modification examples, and application examples, the present disclosure is not limited to the above-described embodiments and the like, and various modifications are possible. It should be noted that the effects described in this specification are only exemplified. Effects of the present disclosure are not limited to the effects described herein. The present disclosure may have effects other than the effects described herein.

(1) For example, the present disclosure may also be configured as follows.

an optical neural network unit that is configured by hardware, and outputs a feature amount map as an intensity distribution by encoding entering light; a photodetection unit that generates image data by photodetecting the feature amount map; and an output unit that outputs the image data to an external communication network. (2) An optical calculation device including:

(3) The optical calculation device according to (1), in which the optical neural network unit includes a plurality of phase difference elements, or a plurality of metasurfaces.

(4) The optical calculation device according (1), in which the optical neural network unit includes a plurality of spatial light modulation liquid crystal elements, or a plurality of MEMS mirrors.

(5) The optical calculation device according (3), further includes a control unit that changes weight of a plurality of the spatial light modulation liquid crystal elements or a plurality of the MEMS mirrors, based on input data from outside.

(6) The optical calculation device according any one of (1) to (4), in which the photodetection unit includes an image sensor or a photodetector array.

the optical calculation device includes: an optical neural network unit that is configured by hardware, and outputs a feature amount map as a light intensity distribution by encoding entering light; a photodetection unit that generates image data by photodetecting the feature amount map; and an output unit that outputs the image data to the information processing device via the communication network, and in which, the information processing device includes a processing unit that processes the image data acquired from the optical calculation device via the communication network. (7) An optical calculation processing system including an optical calculation device and an information processing device, communicatable with each other via a communication network, in which,

(8) The optical calculation processing system according to (6), in which the optical neural network unit includes a plurality of phase difference elements, or a plurality of metasurfaces.

(9) The optical calculation processing system according to (6), in which the optical neural network unit includes a plurality of spatial light modulation liquid crystal elements, or a plurality of MEMS mirrors.

(10) The optical calculation processing system according to (8), further includes a control unit that changes weight of a plurality of the spatial light modulation liquid crystal elements or a plurality of the MEMS mirrors, based on input data from outside.

(11) The optical calculation processing system according to any one of (6) to (9), in which the photodetection unit includes an image sensor or a photodetector array.

(12) The optical calculation processing system according to any one among (6) to (10), in which the processing unit includes a neural network unit that generates reconstructed image data corresponding to the entering light by decoding the image data.

(13) The optical calculation processing system according to (11), further including a light projection unit that generates irradiation light, and causes to generate the entering light from reflected light of the irradiation light.

(14) The optical calculation processing system according to (12), in which the processing unit reconstructs a three-dimensional shape of an object onto which the irradiation light is irradiated, based on the reconstructed image data.

(15) The optical calculation processing system according to (12), in which the processing unit estimates attitude or orientation of an object onto which the irradiation light is irradiated, based on the reconstructed image data.

(16) The optical calculation processing system according to (12), in which the processing unit estimates material of an object onto which the irradiation light is irradiated, based on the reconstructed image data.

the optical calculation device includes: a plurality of the optical neural network units, provided one by one for each of the pieces of reflected lights; and a plurality of the photodetection units, provided one by one for each of the optical neural network units, and in which, the output unit outputs the image data, acquired from each of the photodetection unit, to the information processing device via the communication network. (17) The optical calculation processing system according to (12), in which the light projection unit is configured to cause to generate a plurality of the pieces of reflected light by applying the irradiation light onto an object from a plurality of directions, and in which,

a first neural network unit that generates reconstruction image data corresponding to the entering light by decoding the image data; a second neural network unit that generates classifications of characters included in the entering light by decoding the image data; and a third neural network unit that generates handwritings of characters included in the entering light by decoding the image data. (18) The optical calculation processing system according to any one of (6) to (16), including at least two of:

an optical-address-type spatial light modulation element; and a light source unit that applies coherent light onto the optical-address-type spatial light modulation element, and wherein, the optical-address-type spatial light modulation element is configured to synthesize the coherent light with the entering light by irradiation of the coherent light. (19) The optical calculation processing system according to any one of (6) to (17), in which the optical calculation device further includes:

(20) The optical calculation processing system according to any one of (6) to (18), in which the optical neural network unit includes a plurality of phase difference elements configured by birefringent material.

(21) The optical calculation processing system according to (6), further including a calculation device that derives a differential value serving as a gradient relative to phase distribution of loss function, based on the image data acquired in the photodetection unit and image data acquired by model calculation, and updates phase amount to be outputted to the optical neural network unit, based on the derived differential value.

and further including a calculation device that derives a differential value serving as a gradient relative to phase distribution of loss function, based on the reconstructed image data and image data acquired by model calculation, and updates phase amount to be outputted to the optical neural network unit, based on the derived differential value. (22) The optical calculation processing system according to (6), in which the processing unit includes a neural network unit that generates reconstructed image data corresponding to the entering light by decoding the image data,

(23) The optical calculation processing system according to (6), further including a calculation device that derives a differential value serving as a gradient relative to phase distribution of loss function, based on the image data acquired in the photodetection unit, and updates phase amount to be outputted to the optical neural network unit, based on the derived differential value.

and further including a calculation device that derives a differential value serving as a gradient relative to phase distribution of loss function, based on the reconstructed image data, and updates phase amount to be outputted to the optical neural network unit, based on the derived differential value. The optical calculation processing system according to (6), in which the processing unit includes a neural network unit that generates reconstructed image data corresponding to the entering light by decoding the image data,

The present application claims the benefit of Japanese Priority Patent Application JP2022-119067 filed with the Japan Patent Office on Jul. 26, 2022, the entire contents of which are incorporated herein by reference.

It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and alterations may occur depending on design requirements and other factors insofar as they are within the scope of the appended claims or the equivalents thereof.